Retrieving and organizing web pages by “information unit”
Proceedings of the 10th international conference on World Wide Web
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Multiway SLCA-based keyword search in XML data
Proceedings of the 16th international conference on World Wide Web
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Effective keyword search for valuable lcas over xml documents
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Keyword search on external memory data graphs
Proceedings of the VLDB Endowment
Querying Communities in Relational Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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Keyword search over graphs has a wide array of applications in querying structured, semi-structured and unstructured data. Existing models typically use minimal trees or bounded subgraphs as query answers. While such models emphasize relevancy, they would suffer from incompleteness of information and redundancy among answers, making it difficult for users to effectively explore query answers. To overcome these drawbacks, we propose a novel cluster-based model, where query answers are relevancy-connected clusters. A cluster is a subgraph induced from a maximal set of relevancy-connected nodes. Such clusters are coherent and relevant, yet complete and redundancy free. They can be of arbitrary shape in contrast to the sphere-shaped bounded subgraphs in existing models. We also propose an efficient search algorithm and a corresponding graph index for large, disk-resident data graphs.